Big Data Healthcare: A Leader's Story

Big Data Symposium: Insights and Actions to Reshape the Healthcare Environment

Session #BG4 | Monday, February 11, 2019

Rasu Shrestha, MD, MBA | EVP, Chief Strategy Officer, Atrium Health

@RasuShrestha

Rasu Shrestha, MD, MBA,

EVP, Chief Strategy Officer, Atrium Health

Has no real or apparent conflicts of interest to report.

Conflict of Interest

• Identify challenges behind successful implementation of big data

analytics for improved healthcare outcomes

• Share best practices for big data deployment in a healthcare

setting

• Evaluate metrics and outcome measures used to demonstrate

value of big data healthcare implementation

• Discuss sustenance models for big data initiatives and future

trajectories

Learning Objectives

Hype Cycle for Emerging Technologies

Hype Hope Here

Data:Base

Technology as an Enabler of Better Care

@RasuShrestha

Zak Kohane, Harvard DBMI

Data blind spots

“Existing literature is clear about the

importance of social determinants of health

in improving the health of populations.

These studies uniformly suggest

that nonmedical factors

play a substantially

larger role than do medical

factors in health.”

https://bluecrossmafoundation.org/sites/default/files/download/publicatio

n/Social_Equity_Report_Final.pdf

“Leveraging Social Determinants of Health. What Works?” Prepared for the Blue Cross

Blue Shield of Massachusetts Foundation by Yale Global Health Leadership Institute.

Understanding why patients opt out of interventions is a blind spot for many organizations.

Much like how a gardener sows his seeds, and cares for and

nurtures his garden, managing data, especially at scale, requires

some discipline and, arguably, a good deal of passion.

“

”

Connecting big data to big insights

@RasuShrestha

Data

Information

Knowledge

Insights

Behavior

Change

Nudges

Semantics Harmonizes Data from

Diverse Systems

• Data gathered from

diverse sources needs

to be stored and

reviewed in one

consistent form

• Data needs to be

“normalized and

standardized”

• The data’s content

needs to be preserved:

its context in time,

space and in

relationship with other

data allowing EMRs to

remain viable data

sources

UMLS LOINC NDC RxNorm ICD-9-CM

ICD-10-CM ICD-10-PCS DRG APC APDRG

CPT HCFA HCPCS CDT SNOMED CT HL7

HL7 CVX

OMB

Race/Ethnicity

Standards

Commercial

Interface

Terminologies

Provider

Taxonomy

Revenue Codes

Mapping data from different source systems to national

standards

Harmonization between clinical systems

Creation of ontologies to support clinical use cases around

data exchange, meaningful use and disease management

Data: building blocks to insights

Taming the Big Data Beast

Transformation

Ontology, Logic

Customizability, Design

Harmonization

Semantic Interoperability

Vocabulary

Aggregation

Syntactic Interoperability

Identification

Intelligent

Healthcare

Clinical

Context

Evidence

Based Logic

Role- based

alerts

Analytics at

the point of

care

Person-

Centered

Care

Population

Health

Information Reuse and Integration: Foundation for Accountable Care

Evolution vs Revolution

Is Your BI Solution Meeting Your Needs and Driving Tangible

Outcomes?

KLAS Research, Healthcare Business Intelligence 2018

• Broadly Focused vs Niche

Focused

• Advanced Analytics

Newcomers Making a

Splash

• Open-Source Technology

Threatens Cross-Industry

Behemoths

• Highest Performers

Improve Patient Care and

Reduce Costs

Big Data  Big Impact

•Access to new, diverse data and open datasets are fueling drug

discovery and making clinical trials and research more efficient.

Medical research:

•Wearable devices, online diagnostic tools and genetic sequencing

services hold the promise of better informed and engaged patients.

Daily life:

•Health systems are investing heavily in technology, including machine

learning, which is proving as effective as or more effective than human

diagnosticians.

The patient

experience:

•Telemedicine and health apps make it possible for physicians to see

patients virtually, outside of traditional facilities for increased access

and tailored care.

Ongoing care:

•Health data is allowing doctors to build better patient profiles and

predictive models to more effectively anticipate, diagnose and treat

disease.

Prediction and

prevention:

Adapted in part from: Harnessing the Power of Data in Health, Stanford Medicine Health Trend Report

•Silos and roadblocks prevent effective data sharing but, at the same time,

privacy and security of patient data is paramount.

Data sharing and

security:

•Data privacy and interoperability must be addressed at a legislative level to

create a regulatory environment that encourages innovation and research

while putting patients first.

Policy and legislation:

•Frustrations with the design of electronic medical records undermine the

physician-patient relationship.

Electronic medical

records (EMRs):

•Without proper infrastructure and a data-literate clinical workforce, health

data can only be collected and stored, not leveraged fully.

Skills and training:

•Reliance on reactive health care will hamper physicians’ ability to anticipate,

diagnose and treat disease.

Care models:

Big Data: Challenges/ Opportunities

Adapted in part from: Harnessing the Power of Data in Health, Stanford Medicine Health Trend Report

Rhetoric

Reason

@RasuShrestha

Big Data: It’s about context

BALANCE

Big Data and Big Science  Appropriate Variation in Care

Clinical Redesign

DECREASE variation in population care

Personalized Medicine

INCREASE variation in individual care

Big Science “omics”

Systems Biology

Go from “Syndrome” to precise individual network

Big Data Analytics

Targeted therapy (reduce unnecessary care)

New Models of Care:

Coordinated, team-based, continuous, accountable, affordable, with aligned incentives

Knowledge

Economy

It’s not man vs. machine…

It’s man vs. man and machine

• Embrace of digital thus far

has been a replacement of

analogue

• With Big Data + AI, we

should go beyond

comparison to humans

• Instead, reinvent what it

means to leverage power of

machines at scale, and

augment most humanistic

aspects of care.

@RasuShrestha

What Should We Remember? Too Much Information

Need To Act Fast Not Enough Meaning

Quantitative

AI that produces

complex reports &

documentation. Also

AI in modalities.

Automative

AI that drives

worklists or

diagnostic tasks

Assistive

AI that labels

anatomy; segments;

assists with

diagnosis

Qualitative

AI that qualifies

metrics/ value for

the system across

broader parameters

@RasuShrestha

CheXNet

outperforms the

best published

results on all 14

pathologies in the

ChestX-ray14

dataset.

Core consideration as

Big Data + AI veers

towards ‘escape

velocity’:

• Data science training

• Focus on delivery

mechanisms and

workflow, not just

apps

• Become more

purposeful

• Think more

holistically

• Use Big Data + AI to

humanize care

@RasuShrestha

T H A N K Y O U

Rasu Shrestha MD MBA

Chief Strategy Officer | Atrium Health

@RasuShrestha